Temperature

Days

The methodology behind these projections is described in full in Hsiang, Kopp, Jina, Rising et al., 2017. The climate projection methodology is described in full in Rasmussen et al. (2016). All daily projections from this analysis are freely available online here.

Climate Projections

The climate projections show on this map are based on Representative Concentration Pathway 2.6, 4.5, and 8.5 (van Vuuren et al., 2012) experiments run by global climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) exercise (Taylor et al., 2012). In particular, we used downscaled CMIP5 climate projections prepared by the US Bureau of Reclamation (Brekke et al., 2013). This dataset is bias-corrected and downscaled using the Bias-Correction Spatial Disaggregation (BCSD) method (Thrasher et al., 2012).

CMIP5 projections do not inherently constitute a probability distribution; rather, they are an ensemble of opportunity, composed of runs conducted by climate modeling teams participating on a voluntary basis and running models that roughly represent ‘best-estimate’ projections of climate behavior. To produce a probabilistic ensemble, we used the Surrogate Model/Mixed Ensemble (SMME) method of Rasmussen et al. (2016). This method weights projections by comparing their global mean surface temperature projections to those of a probabilistic simple climate model, in this case (as in Rasmussen et al., 2016) the MAGICC6 model (Meinshausen et al., 2011). The target global mean temperature distributions for 2080-2099 used were identical to those of Rasmussen et al. (2016). As in that paper, potential temperature outcomes produced the probabilistic simple climate model but not represented within the downscaled CMIP5 dataset were represented by ‘model surrogates’, produced using linear pattern scaling, with residuals added to represent high-frequency variability and non-linearities.

The gridded projections were aggregated to regional estimates by first transforming the daily min, average, or maximum temperature at the grid scale, then aggregating to regions using a weighted average. Annual and seasonal average temperatures are weighted using the shares of each region’s land area within each grid cell; estimates of days above 95F and below 32F are weighted using the shares of each region’s population within each grid cell.

Temperature

Days

The climate projection methodology is described in full in Rasmussen et al. (2016). These projections are based on Representative Concentration Pathway 4.5 and 8.5 (van Vuuren et al., 2012) experiments run by global climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) exercise (Taylor et al., 2012). In particular, we used the NASA Earth Exchange Global Daily Downscaled Projection (NEX-GDDP) dataset, prepared by the Climate Analytics Group and NASA Ames Research Center using the NASA Earth Exchange, and distributed by the NASA Center for Climate Simulation (NCCS). This dataset is bias-corrected and downscaled using the Bias-Correction Spatial Disaggregation (BCSD) method (Thrasher et al., 2012).

CMIP5 projections do not inherently constitute a probability distribution; rather, they are an ensemble of opportunity, composed of runs conducted by climate modeling teams participating on a voluntary basis and running models that roughly represent ‘best-estimate’ projections of climate behavior. To produce a probabilistic ensemble, we used the Surrogate Model/Mixed Ensemble (SMME) method of Rasmussen et al. (2016). This method weights projections by comparing their global mean surface temperature projections to those of a probabilistic simple climate model, in this case (as in Rasmussen et al., 2016) the MAGICC6 model (Meinshausen et al., 2011). The target global mean temperature distributions for 2080-2099 used were identical to those of Rasmussen et al. (2016). As in that paper, potential temperature outcomes produced the probabilistic simple climate model but not represented within the NEX-GDDP dataset were represented by ‘model surrogates’, produced using linear pattern scaling, with residuals added to represent high-frequency variability and non-linearities.

The gridded projections were aggregated to regional estimates by first transforming the daily min, average, or maximum temperature at the grid scale, then aggregating to regions using a weighted average. All variables are weighted using the shares of each region’s land area within each grid cell.